import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import re
from collections import Counter
from sklearn.decomposition import TruncatedSVD, PCA
from sklearn.feature_selection import SelectKBest
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
sns.set(style='darkgrid', palette='pastel')
pd.options.display.max_columns = None
data = pd.read_csv('../data/processed/0_cleaned.csv')
data_backup = data.copy()
label = data[['Value', 'Wage']]
data.drop(columns=['Value', 'Wage'], inplace=True)
data.head(5)
| Age | Nationality | Overall | Potential | Club | Special | Preferred Foot | International Reputation | Weak Foot | Skill Moves | Work Rate | Body Type | Position | Jersey Number | Height | Weight | LS | ST | RS | LW | LF | CF | RF | RW | LAM | CAM | RAM | LM | LCM | CM | RCM | RM | LWB | LDM | CDM | RDM | RWB | LB | LCB | CB | RCB | RB | Crossing | Finishing | HeadingAccuracy | ShortPassing | Volleys | Dribbling | Curve | FKAccuracy | LongPassing | BallControl | Acceleration | SprintSpeed | Agility | Reactions | Balance | ShotPower | Jumping | Stamina | Strength | LongShots | Aggression | Interceptions | Positioning | Vision | Penalties | Composure | Marking | StandingTackle | SlidingTackle | GKDiving | GKHandling | GKKicking | GKPositioning | GKReflexes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 31.0 | Argentina | 94.0 | 94.0 | FC Barcelona | 2202.0 | Left | 5.0 | 4.0 | 4.0 | Medium/ Medium | Messi | RF | 10.0 | 170.18 | 159.0 | 88.0 | 88.0 | 88.0 | 92.0 | 93.0 | 93.0 | 93.0 | 92.0 | 93.0 | 93.0 | 93.0 | 91.0 | 84.0 | 84.0 | 84.0 | 91.0 | 64.0 | 61.0 | 61.0 | 61.0 | 64.0 | 59.0 | 47.0 | 47.0 | 47.0 | 59.0 | 84.0 | 95.0 | 70.0 | 90.0 | 86.0 | 97.0 | 93.0 | 94.0 | 87.0 | 96.0 | 91.0 | 86.0 | 91.0 | 95.0 | 95.0 | 85.0 | 68.0 | 72.0 | 59.0 | 94.0 | 48.0 | 22.0 | 94.0 | 94.0 | 75.0 | 96.0 | 33.0 | 28.0 | 26.0 | 6.0 | 11.0 | 15.0 | 14.0 | 8.0 |
| 1 | 33.0 | Portugal | 94.0 | 94.0 | Juventus | 2228.0 | Right | 5.0 | 4.0 | 5.0 | High/ Low | C. Ronaldo | ST | 7.0 | 187.96 | 183.0 | 91.0 | 91.0 | 91.0 | 89.0 | 90.0 | 90.0 | 90.0 | 89.0 | 88.0 | 88.0 | 88.0 | 88.0 | 81.0 | 81.0 | 81.0 | 88.0 | 65.0 | 61.0 | 61.0 | 61.0 | 65.0 | 61.0 | 53.0 | 53.0 | 53.0 | 61.0 | 84.0 | 94.0 | 89.0 | 81.0 | 87.0 | 88.0 | 81.0 | 76.0 | 77.0 | 94.0 | 89.0 | 91.0 | 87.0 | 96.0 | 70.0 | 95.0 | 95.0 | 88.0 | 79.0 | 93.0 | 63.0 | 29.0 | 95.0 | 82.0 | 85.0 | 95.0 | 28.0 | 31.0 | 23.0 | 7.0 | 11.0 | 15.0 | 14.0 | 11.0 |
| 2 | 26.0 | Brazil | 92.0 | 93.0 | Paris Saint-Germain | 2143.0 | Right | 5.0 | 5.0 | 5.0 | High/ Medium | Neymar | LW | 10.0 | 175.26 | 150.0 | 84.0 | 84.0 | 84.0 | 89.0 | 89.0 | 89.0 | 89.0 | 89.0 | 89.0 | 89.0 | 89.0 | 88.0 | 81.0 | 81.0 | 81.0 | 88.0 | 65.0 | 60.0 | 60.0 | 60.0 | 65.0 | 60.0 | 47.0 | 47.0 | 47.0 | 60.0 | 79.0 | 87.0 | 62.0 | 84.0 | 84.0 | 96.0 | 88.0 | 87.0 | 78.0 | 95.0 | 94.0 | 90.0 | 96.0 | 94.0 | 84.0 | 80.0 | 61.0 | 81.0 | 49.0 | 82.0 | 56.0 | 36.0 | 89.0 | 87.0 | 81.0 | 94.0 | 27.0 | 24.0 | 33.0 | 9.0 | 9.0 | 15.0 | 15.0 | 11.0 |
| 3 | 27.0 | Spain | 91.0 | 93.0 | Manchester United | 1471.0 | Right | 4.0 | 3.0 | 1.0 | Medium/ Medium | Lean | GK | 1.0 | 193.04 | 168.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 17.0 | 13.0 | 21.0 | 50.0 | 13.0 | 18.0 | 21.0 | 19.0 | 51.0 | 42.0 | 57.0 | 58.0 | 60.0 | 90.0 | 43.0 | 31.0 | 67.0 | 43.0 | 64.0 | 12.0 | 38.0 | 30.0 | 12.0 | 68.0 | 40.0 | 68.0 | 15.0 | 21.0 | 13.0 | 90.0 | 85.0 | 87.0 | 88.0 | 94.0 |
| 4 | 27.0 | Belgium | 91.0 | 92.0 | Manchester City | 2281.0 | Right | 4.0 | 5.0 | 4.0 | High/ High | Normal | RCM | 7.0 | 180.34 | 154.0 | 82.0 | 82.0 | 82.0 | 87.0 | 87.0 | 87.0 | 87.0 | 87.0 | 88.0 | 88.0 | 88.0 | 88.0 | 87.0 | 87.0 | 87.0 | 88.0 | 77.0 | 77.0 | 77.0 | 77.0 | 77.0 | 73.0 | 66.0 | 66.0 | 66.0 | 73.0 | 93.0 | 82.0 | 55.0 | 92.0 | 82.0 | 86.0 | 85.0 | 83.0 | 91.0 | 91.0 | 78.0 | 76.0 | 79.0 | 91.0 | 77.0 | 91.0 | 63.0 | 90.0 | 75.0 | 91.0 | 76.0 | 61.0 | 87.0 | 94.0 | 79.0 | 88.0 | 68.0 | 58.0 | 51.0 | 15.0 | 13.0 | 5.0 | 10.0 | 13.0 |
label.isnull().sum()
Value 0 Wage 0 dtype: int64
categorical_columns = np.asarray([not np.issubdtype(data[col].dtype, np.number) for col in data.columns], dtype=np.bool)
cat_col_names = [col for col in data.columns if not np.issubdtype(data[col].dtype, np.number)]
num_col_names = [col for col in data.columns if np.issubdtype(data[col].dtype, np.number)]
categorical_data = data.iloc[:, categorical_columns]
non_categorical = data.iloc[:, ~categorical_columns]
categorical_data.reset_index(inplace=True, drop=True)
non_categorical.reset_index(inplace=True, drop=True)
cardinality_threshold = 10
cols_to_reduce_dim = []
for c in cat_col_names:
levels = categorical_data[c].drop_duplicates().shape[0]
if levels > cardinality_threshold:
cols_to_reduce_dim.append(c)
df_reduce = categorical_data[cols_to_reduce_dim]
df_reduce = pd.get_dummies(df_reduce)
svd = PCA(n_components=0.85)
df_reduce = svd.fit_transform(df_reduce)
column_names = []
for col in range(df_reduce.shape[1]):
column_names.append('reduced_col_{}'.format(col))
df_reduce = pd.DataFrame(df_reduce, columns=column_names)
df_dummies = categorical_data.drop(cols_to_reduce_dim, axis=1)
df_dummies = pd.get_dummies(df_dummies)
df_dummies = df_dummies.join(df_reduce)
df_dummies = df_dummies.join(non_categorical)
encoder = OrdinalEncoder()
encoder.fit(categorical_data)
column_names = categorical_data.columns
categorical_data = encoder.transform(categorical_data)
df_ordinal = pd.DataFrame(categorical_data, columns=column_names)
del categorical_data
df_ordinal = non_categorical.join(df_ordinal)
del non_categorical
scaler = StandardScaler()
column_names = df_ordinal.columns
df_ordinal = scaler.fit_transform(df_ordinal)
df_ordinal = pd.DataFrame(df_ordinal, columns=column_names)
df_ordinal.head()
del column_names
scaler = StandardScaler()
column_names = df_dummies.columns
df_dummies = scaler.fit_transform(df_dummies)
df_dummies = pd.DataFrame(df_dummies, columns=column_names)
df_dummies.head()
del column_names
df_dummies.head()
| Preferred Foot_Left | Preferred Foot_Right | Work Rate_High/ High | Work Rate_High/ Low | Work Rate_High/ Medium | Work Rate_Low/ High | Work Rate_Low/ Low | Work Rate_Low/ Medium | Work Rate_Medium/ High | Work Rate_Medium/ Low | Work Rate_Medium/ Medium | Body Type_Akinfenwa | Body Type_C. Ronaldo | Body Type_Courtois | Body Type_Lean | Body Type_Messi | Body Type_Neymar | Body Type_Normal | Body Type_PLAYER_BODY_TYPE_25 | Body Type_Shaqiri | Body Type_Stocky | reduced_col_0 | reduced_col_1 | reduced_col_2 | reduced_col_3 | reduced_col_4 | reduced_col_5 | reduced_col_6 | reduced_col_7 | reduced_col_8 | reduced_col_9 | reduced_col_10 | reduced_col_11 | reduced_col_12 | reduced_col_13 | reduced_col_14 | reduced_col_15 | reduced_col_16 | reduced_col_17 | reduced_col_18 | reduced_col_19 | reduced_col_20 | reduced_col_21 | reduced_col_22 | reduced_col_23 | reduced_col_24 | reduced_col_25 | reduced_col_26 | reduced_col_27 | reduced_col_28 | reduced_col_29 | reduced_col_30 | reduced_col_31 | reduced_col_32 | reduced_col_33 | reduced_col_34 | reduced_col_35 | reduced_col_36 | reduced_col_37 | reduced_col_38 | reduced_col_39 | reduced_col_40 | reduced_col_41 | reduced_col_42 | reduced_col_43 | reduced_col_44 | reduced_col_45 | reduced_col_46 | reduced_col_47 | reduced_col_48 | reduced_col_49 | reduced_col_50 | reduced_col_51 | reduced_col_52 | reduced_col_53 | reduced_col_54 | reduced_col_55 | reduced_col_56 | reduced_col_57 | reduced_col_58 | reduced_col_59 | reduced_col_60 | reduced_col_61 | reduced_col_62 | reduced_col_63 | reduced_col_64 | reduced_col_65 | reduced_col_66 | reduced_col_67 | reduced_col_68 | reduced_col_69 | reduced_col_70 | reduced_col_71 | reduced_col_72 | reduced_col_73 | reduced_col_74 | reduced_col_75 | reduced_col_76 | reduced_col_77 | reduced_col_78 | reduced_col_79 | reduced_col_80 | reduced_col_81 | reduced_col_82 | reduced_col_83 | reduced_col_84 | reduced_col_85 | reduced_col_86 | reduced_col_87 | reduced_col_88 | reduced_col_89 | reduced_col_90 | reduced_col_91 | reduced_col_92 | reduced_col_93 | reduced_col_94 | reduced_col_95 | reduced_col_96 | reduced_col_97 | reduced_col_98 | reduced_col_99 | reduced_col_100 | reduced_col_101 | reduced_col_102 | reduced_col_103 | reduced_col_104 | reduced_col_105 | reduced_col_106 | reduced_col_107 | reduced_col_108 | reduced_col_109 | reduced_col_110 | reduced_col_111 | reduced_col_112 | reduced_col_113 | reduced_col_114 | reduced_col_115 | reduced_col_116 | reduced_col_117 | reduced_col_118 | reduced_col_119 | reduced_col_120 | reduced_col_121 | reduced_col_122 | reduced_col_123 | reduced_col_124 | reduced_col_125 | reduced_col_126 | reduced_col_127 | reduced_col_128 | reduced_col_129 | reduced_col_130 | reduced_col_131 | reduced_col_132 | reduced_col_133 | reduced_col_134 | reduced_col_135 | reduced_col_136 | reduced_col_137 | reduced_col_138 | reduced_col_139 | reduced_col_140 | reduced_col_141 | reduced_col_142 | reduced_col_143 | reduced_col_144 | reduced_col_145 | reduced_col_146 | reduced_col_147 | reduced_col_148 | reduced_col_149 | reduced_col_150 | reduced_col_151 | reduced_col_152 | reduced_col_153 | reduced_col_154 | reduced_col_155 | reduced_col_156 | reduced_col_157 | reduced_col_158 | reduced_col_159 | reduced_col_160 | reduced_col_161 | reduced_col_162 | reduced_col_163 | reduced_col_164 | reduced_col_165 | reduced_col_166 | reduced_col_167 | reduced_col_168 | reduced_col_169 | reduced_col_170 | reduced_col_171 | reduced_col_172 | reduced_col_173 | reduced_col_174 | reduced_col_175 | reduced_col_176 | reduced_col_177 | reduced_col_178 | reduced_col_179 | reduced_col_180 | reduced_col_181 | reduced_col_182 | reduced_col_183 | reduced_col_184 | reduced_col_185 | reduced_col_186 | reduced_col_187 | reduced_col_188 | reduced_col_189 | reduced_col_190 | reduced_col_191 | reduced_col_192 | reduced_col_193 | reduced_col_194 | reduced_col_195 | reduced_col_196 | reduced_col_197 | reduced_col_198 | reduced_col_199 | reduced_col_200 | reduced_col_201 | reduced_col_202 | reduced_col_203 | reduced_col_204 | reduced_col_205 | reduced_col_206 | reduced_col_207 | reduced_col_208 | reduced_col_209 | reduced_col_210 | reduced_col_211 | reduced_col_212 | reduced_col_213 | reduced_col_214 | reduced_col_215 | reduced_col_216 | reduced_col_217 | reduced_col_218 | reduced_col_219 | reduced_col_220 | reduced_col_221 | reduced_col_222 | reduced_col_223 | reduced_col_224 | reduced_col_225 | reduced_col_226 | reduced_col_227 | reduced_col_228 | reduced_col_229 | reduced_col_230 | reduced_col_231 | reduced_col_232 | reduced_col_233 | reduced_col_234 | reduced_col_235 | reduced_col_236 | reduced_col_237 | reduced_col_238 | reduced_col_239 | reduced_col_240 | reduced_col_241 | reduced_col_242 | reduced_col_243 | reduced_col_244 | reduced_col_245 | reduced_col_246 | reduced_col_247 | reduced_col_248 | reduced_col_249 | reduced_col_250 | reduced_col_251 | reduced_col_252 | reduced_col_253 | reduced_col_254 | reduced_col_255 | reduced_col_256 | reduced_col_257 | reduced_col_258 | reduced_col_259 | reduced_col_260 | reduced_col_261 | reduced_col_262 | reduced_col_263 | reduced_col_264 | reduced_col_265 | reduced_col_266 | reduced_col_267 | reduced_col_268 | reduced_col_269 | reduced_col_270 | reduced_col_271 | reduced_col_272 | reduced_col_273 | reduced_col_274 | reduced_col_275 | reduced_col_276 | reduced_col_277 | reduced_col_278 | reduced_col_279 | reduced_col_280 | reduced_col_281 | reduced_col_282 | reduced_col_283 | reduced_col_284 | reduced_col_285 | reduced_col_286 | reduced_col_287 | reduced_col_288 | reduced_col_289 | reduced_col_290 | reduced_col_291 | reduced_col_292 | reduced_col_293 | reduced_col_294 | reduced_col_295 | reduced_col_296 | reduced_col_297 | reduced_col_298 | reduced_col_299 | reduced_col_300 | reduced_col_301 | reduced_col_302 | reduced_col_303 | reduced_col_304 | reduced_col_305 | reduced_col_306 | reduced_col_307 | reduced_col_308 | reduced_col_309 | reduced_col_310 | reduced_col_311 | reduced_col_312 | reduced_col_313 | reduced_col_314 | reduced_col_315 | reduced_col_316 | reduced_col_317 | reduced_col_318 | reduced_col_319 | reduced_col_320 | reduced_col_321 | reduced_col_322 | reduced_col_323 | reduced_col_324 | reduced_col_325 | reduced_col_326 | reduced_col_327 | reduced_col_328 | reduced_col_329 | reduced_col_330 | reduced_col_331 | reduced_col_332 | reduced_col_333 | reduced_col_334 | reduced_col_335 | reduced_col_336 | reduced_col_337 | reduced_col_338 | reduced_col_339 | reduced_col_340 | reduced_col_341 | reduced_col_342 | reduced_col_343 | reduced_col_344 | reduced_col_345 | reduced_col_346 | reduced_col_347 | reduced_col_348 | reduced_col_349 | reduced_col_350 | reduced_col_351 | reduced_col_352 | reduced_col_353 | reduced_col_354 | reduced_col_355 | reduced_col_356 | reduced_col_357 | reduced_col_358 | reduced_col_359 | reduced_col_360 | reduced_col_361 | reduced_col_362 | reduced_col_363 | reduced_col_364 | reduced_col_365 | reduced_col_366 | reduced_col_367 | reduced_col_368 | reduced_col_369 | reduced_col_370 | reduced_col_371 | reduced_col_372 | reduced_col_373 | reduced_col_374 | reduced_col_375 | reduced_col_376 | reduced_col_377 | reduced_col_378 | reduced_col_379 | reduced_col_380 | reduced_col_381 | reduced_col_382 | reduced_col_383 | reduced_col_384 | reduced_col_385 | reduced_col_386 | reduced_col_387 | reduced_col_388 | reduced_col_389 | reduced_col_390 | reduced_col_391 | reduced_col_392 | reduced_col_393 | reduced_col_394 | reduced_col_395 | reduced_col_396 | reduced_col_397 | reduced_col_398 | reduced_col_399 | reduced_col_400 | reduced_col_401 | reduced_col_402 | reduced_col_403 | reduced_col_404 | reduced_col_405 | reduced_col_406 | reduced_col_407 | reduced_col_408 | reduced_col_409 | reduced_col_410 | reduced_col_411 | reduced_col_412 | reduced_col_413 | reduced_col_414 | reduced_col_415 | reduced_col_416 | reduced_col_417 | reduced_col_418 | reduced_col_419 | reduced_col_420 | reduced_col_421 | reduced_col_422 | reduced_col_423 | reduced_col_424 | reduced_col_425 | reduced_col_426 | reduced_col_427 | reduced_col_428 | reduced_col_429 | reduced_col_430 | reduced_col_431 | reduced_col_432 | reduced_col_433 | reduced_col_434 | reduced_col_435 | reduced_col_436 | Age | Overall | Potential | Special | International Reputation | Weak Foot | Skill Moves | Jersey Number | Height | Weight | LS | ST | RS | LW | LF | CF | RF | RW | LAM | CAM | RAM | LM | LCM | CM | RCM | RM | LWB | LDM | CDM | RDM | RWB | LB | LCB | CB | RCB | RB | Crossing | Finishing | HeadingAccuracy | ShortPassing | Volleys | Dribbling | Curve | FKAccuracy | LongPassing | BallControl | Acceleration | SprintSpeed | Agility | Reactions | Balance | ShotPower | Jumping | Stamina | Strength | LongShots | Aggression | Interceptions | Positioning | Vision | Penalties | Composure | Marking | StandingTackle | SlidingTackle | GKDiving | GKHandling | GKKicking | GKPositioning | GKReflexes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.819966 | -1.819966 | -0.243320 | -0.200086 | -0.460142 | -0.157398 | -0.043311 | -0.159226 | -0.320339 | -0.221602 | 0.922535 | -0.007421 | -0.007421 | -0.007421 | -0.739256 | 134.751623 | -0.007421 | -1.183518 | -0.007421 | -0.007421 | -0.258813 | -0.025030 | -0.124137 | 0.043661 | -0.489503 | 0.076626 | -0.052710 | 0.045809 | -0.696539 | 0.254391 | -1.116797 | -1.076762 | 0.449501 | 3.152894 | 1.795082 | 0.980016 | 0.278538 | 0.393420 | 0.053375 | 0.385782 | 0.127697 | 0.457118 | 0.091012 | -0.003341 | 0.100609 | -0.026914 | -0.062550 | -0.038607 | 0.021641 | 0.029073 | -0.010898 | -0.012549 | -0.010883 | -0.034975 | -0.153374 | -0.018113 | -0.056407 | 0.063416 | 0.007848 | -0.077980 | -0.149507 | 0.387160 | -0.204118 | 0.021048 | 0.030567 | -0.068259 | 0.290932 | 0.124257 | -0.175111 | 0.068023 | -0.000238 | -0.026643 | -0.130464 | -0.113250 | -0.065391 | -0.025833 | 0.042499 | -0.159169 | 0.293210 | 0.920934 | 0.159750 | 0.043026 | -0.080303 | 0.067361 | -0.138927 | 0.303237 | 0.429450 | 0.316550 | -0.172149 | -0.153590 | -0.046946 | -0.050547 | -0.049805 | -0.029740 | -0.001275 | -0.007572 | -0.010982 | -0.173994 | 0.016092 | -0.126822 | -0.189650 | -0.079487 | -0.120204 | -0.180438 | 0.253743 | -0.493973 | 0.735913 | 0.004201 | 1.103071 | -0.022659 | -0.204418 | -3.121383 | -2.655840 | 12.075566 | -11.710633 | -0.778650 | -12.673207 | 2.489642 | 1.884204 | 0.715373 | -4.532138 | 0.480582 | 1.045607 | 2.115204 | -3.982130 | 2.712623 | -1.487675 | -1.367518 | 0.599605 | -6.291128 | 0.028271 | 2.856445 | -4.943365 | 3.262012 | 0.698759 | 0.452892 | 3.169241 | 0.733207 | 0.189608 | 0.014154 | -0.535051 | -0.108434 | 0.201775 | 0.370942 | 0.469042 | 0.740614 | 0.502943 | 0.955104 | 0.536674 | 0.063468 | -0.200761 | 0.358547 | 0.392161 | 0.301760 | -0.205254 | 1.360603 | -0.295316 | 0.139560 | 0.245110 | 1.115577 | 0.721629 | -0.205635 | 0.452851 | 0.520400 | 1.370042 | 0.514755 | -0.662536 | 0.561032 | -0.023284 | 0.150546 | -0.121182 | -0.042828 | -0.133688 | 0.062027 | 0.014256 | 0.026487 | -0.075329 | -0.064654 | 0.017431 | 0.042805 | 0.022480 | 0.038812 | 0.030842 | -0.006894 | -0.015514 | 0.013093 | -0.015921 | -0.004244 | -0.039522 | -0.031782 | -0.005264 | 0.017619 | -0.011958 | 0.030580 | 0.003526 | 0.034625 | -0.018188 | 0.001857 | 0.049740 | -0.022147 | 0.025406 | 0.002060 | 0.010423 | -0.035769 | 0.066609 | 0.062303 | 0.048421 | 0.005261 | 0.006434 | -0.001493 | 0.018563 | -0.005977 | -0.034769 | 0.078932 | 0.042463 | -0.069521 | 0.008345 | 0.070999 | 0.064913 | 0.125622 | -0.167970 | 0.029444 | 0.049896 | -0.027177 | -0.079337 | -0.037055 | 0.104196 | 0.063288 | 0.099125 | -0.213047 | -0.108603 | -0.012141 | -0.155789 | -0.137741 | -0.113855 | 0.055748 | 0.264588 | -0.138650 | -0.230307 | 0.025343 | -0.014355 | 0.371855 | -0.226583 | 0.022212 | -0.218888 | 0.097323 | -0.539793 | -0.186057 | 0.174866 | 0.040428 | 0.411391 | -0.193008 | -0.312114 | 0.529564 | -0.795016 | 0.107424 | 0.376049 | 0.333085 | 0.033996 | -0.077841 | 0.056613 | -0.071118 | 0.517264 | -0.178085 | -0.084891 | -0.164599 | -0.380301 | -0.363339 | -0.341733 | 0.480933 | 0.255668 | -0.144997 | 0.058395 | 0.147948 | -0.047594 | -0.273265 | -0.139852 | -0.016671 | -0.080807 | 0.155682 | -0.267176 | 0.101779 | -0.001658 | -0.240250 | -0.050075 | 0.098974 | 0.064657 | -0.005191 | -0.210237 | 0.210051 | 0.143640 | -0.110922 | 0.127793 | -0.228330 | -0.250901 | -0.115086 | -0.031032 | 0.456749 | 0.273699 | -0.183640 | 1.058342 | 0.367576 | 0.533280 | -0.588887 | -0.099736 | 0.258391 | -0.694769 | -0.584398 | 0.553087 | 0.170711 | 0.132539 | -0.534545 | -0.458022 | 0.345254 | -0.115451 | 0.096934 | -0.377557 | -0.190382 | 0.270146 | -0.024930 | 0.151907 | -0.139731 | 0.000784 | -0.011357 | -0.045590 | 0.014198 | 0.003449 | -0.006683 | 0.098726 | -0.025701 | -0.021375 | 0.007932 | -0.007125 | -0.007166 | 0.032126 | 0.034834 | -0.016020 | -0.006656 | -0.009631 | -0.005452 | 0.101521 | -0.001392 | -0.035765 | -0.040548 | -0.012966 | -0.026198 | 0.016646 | 0.015037 | 0.018060 | 0.005419 | -0.000264 | -0.008489 | 0.002495 | -0.009407 | 0.009845 | -0.008405 | -0.005385 | 0.001375 | -0.000913 | -0.000753 | 0.000207 | 0.000363 | -1.363825e-13 | -1.448941e-13 | -7.334690e-14 | -2.202886e-13 | -1.315246e-13 | 3.045389e-14 | 5.116986e-14 | -1.132121e-13 | 6.418524e-15 | 2.731428e-14 | -2.437712e-14 | 2.464209e-14 | -1.299866e-13 | -1.767396e-13 | 6.222596e-15 | -8.049848e-14 | 1.444140e-13 | -9.275325e-14 | -1.132351e-13 | -1.626771e-13 | 5.938240e-14 | -2.083328e-13 | 1.282210e-13 | 1.428713e-13 | -1.582860e-13 | -6.093101e-13 | -1.462930e-13 | -1.788843e-13 | 5.282681e-14 | -0.000133 | 0.000393 | -0.000119 | 0.000785 | 0.000306 | -0.000736 | 0.004750 | -0.005332 | -0.002994 | 0.009795 | 0.001406 | -0.004008 | -0.011407 | 0.033392 | -0.036956 | 0.040971 | -0.019653 | -0.022804 | 0.052971 | 0.038464 | 0.054940 | 0.023719 | 0.058886 | -0.014836 | -0.045852 | -0.057994 | -0.051461 | 0.015118 | 0.029555 | -0.075096 | -0.007399 | 0.086235 | -0.020287 | -0.163265 | -0.057984 | -0.181406 | -0.097463 | -0.130997 | 0.133144 | 0.049848 | 0.067051 | 0.053665 | -0.134891 | -0.022085 | 0.310444 | -0.202646 | 0.064657 | 0.078541 | 0.156599 | -0.157880 | 0.059175 | 0.136121 | 0.081225 | 0.126736 | -0.045943 | 0.263086 | 0.313761 | -0.161997 | -0.257737 | 0.750700 | -0.087125 | -0.008060 | 0.315903 | 0.290849 | -0.078487 | 0.158924 | -0.068475 | 1.258441 | 4.013364 | 3.697415 | 2.213984 | 9.864420 | 1.593944 | 2.167171 | -0.597693 | -1.646010 | -0.447583 | 1.818665 | 1.818665 | 1.818665 | 1.901392 | 1.973099 | 1.973099 | 1.973099 | 1.901392 | 1.957564 | 1.957564 | 1.957564 | 1.828702 | 1.603458 | 1.603458 | 1.603458 | 1.828702 | 0.646400 | 0.519988 | 0.519988 | 0.519988 | 0.646400 | 0.427328 | -0.114632 | -0.114632 | -0.114632 | 0.427328 | 1.865922 | 2.532567 | 1.018552 | 2.130287 | 2.435355 | 2.201445 | 2.491426 | 2.925736 | 2.237037 | 2.255198 | 1.767621 | 1.452129 | 1.862187 | 3.680643 | 2.195382 | 1.713704 | 0.246247 | 0.552403 | -0.502679 | 2.434582 | -0.453087 | -1.193365 | 2.255244 | 2.869906 | 1.684414 | 3.266205 | -0.717531 | -0.909268 | -0.923569 | -0.599961 | -0.318908 | -0.074659 | -0.140241 | -0.485161 |
| 1 | -0.549461 | 0.549461 | -0.243320 | 4.997854 | -0.460142 | -0.157398 | -0.043311 | -0.159226 | -0.320339 | -0.221602 | -1.083970 | -0.007421 | 134.751623 | -0.007421 | -0.739256 | -0.007421 | -0.007421 | -1.183518 | -0.007421 | -0.007421 | -0.258813 | 2.398178 | 1.008121 | 0.682379 | -0.138231 | 0.163670 | -0.075588 | -0.201962 | 0.072392 | -0.071780 | 0.036250 | -0.205407 | -0.066710 | -0.124488 | -0.237288 | -0.449089 | -0.198792 | -0.230692 | -0.100495 | -0.520811 | -0.308699 | 0.001603 | -0.338568 | 0.146506 | -0.083066 | -0.160981 | 0.125811 | 0.812519 | -0.425463 | -0.497097 | 0.695280 | -0.265159 | -0.043956 | 0.224522 | -0.483118 | 1.594094 | 5.865933 | -3.389932 | -0.532820 | -0.956426 | -0.847770 | -0.008147 | -0.316175 | -0.057831 | -0.181831 | -0.370449 | 0.207443 | 0.024829 | -0.708669 | 0.382464 | 0.101958 | 0.054491 | -0.118635 | -0.198574 | -0.064656 | -0.106369 | -0.044582 | -0.136223 | -0.088453 | 0.118739 | 0.081802 | 0.058662 | -0.120243 | -0.114975 | -0.147305 | -0.032455 | 0.012907 | 0.010350 | -0.070376 | -0.125345 | -0.100013 | 0.178665 | -0.328442 | -0.290382 | 0.004605 | -0.098633 | -0.106563 | -0.174978 | 0.003670 | -0.171749 | -0.030454 | -0.160686 | -0.088235 | -0.049885 | -0.146354 | 0.020687 | 0.091310 | -0.085712 | 0.191194 | -0.036853 | -0.227961 | -0.158188 | -0.116416 | 0.114118 | -0.158092 | -0.031987 | 0.164136 | -0.064716 | 0.075683 | -0.058872 | 0.031380 | -0.085117 | 0.003234 | -0.060703 | -0.005989 | -0.019529 | 0.132114 | -0.138066 | -0.043068 | -0.048845 | 0.105723 | 0.006907 | 0.005168 | 0.096942 | -0.353603 | -0.180423 | -0.201193 | 0.016554 | 0.043206 | -0.001453 | -0.022829 | 0.147152 | -0.032314 | 0.070600 | 0.136919 | -0.069818 | -0.016255 | 0.201512 | 0.109765 | 0.006962 | -0.058626 | 0.131766 | -0.206312 | -0.230839 | -0.128875 | 1.233090 | -0.524567 | -0.069288 | -0.039048 | -0.014911 | 0.167241 | 0.271031 | 0.178742 | -0.034539 | 0.449607 | 0.080966 | -0.173549 | 0.044169 | -0.035222 | 0.062597 | -0.051253 | -0.038977 | -0.049965 | -0.016291 | -0.002645 | -0.104362 | -0.011617 | 0.002160 | -0.072223 | 0.025964 | -0.013008 | 0.002857 | 0.043406 | 0.007321 | -0.005303 | -0.000699 | 0.013769 | 0.000660 | -0.016724 | 0.001320 | 0.000178 | -0.000552 | -0.028578 | -0.005265 | 0.003328 | 0.004945 | -0.008196 | -0.000306 | 0.000200 | 0.010084 | 0.019227 | 0.010336 | 0.009096 | 0.043233 | -0.011131 | -0.024469 | 0.010955 | 0.035454 | -0.022837 | 0.021715 | -0.057549 | 0.025239 | 0.028902 | -0.049782 | -0.006342 | -0.026603 | -0.002942 | -0.057084 | 0.022124 | 0.003381 | -0.057972 | -0.072170 | -0.071880 | 0.039197 | 0.048447 | 0.150653 | 0.123678 | 0.009686 | -0.013250 | 0.056986 | -0.036353 | -0.152211 | 0.047230 | -0.059988 | -0.054894 | -0.083391 | 0.099022 | -0.198829 | 0.035494 | 0.136022 | 0.047292 | -0.076347 | -0.002451 | 0.138738 | 0.030963 | -0.029348 | -0.375283 | -0.241350 | -0.297130 | -0.205267 | 0.262311 | 0.002931 | -0.148200 | 0.416982 | -0.435787 | 0.010200 | 0.164588 | 0.326081 | -0.142780 | -0.149021 | 0.071213 | -0.087520 | 0.018125 | -0.111020 | 0.044942 | -0.024689 | -0.180849 | -0.174874 | -0.224991 | -0.183709 | 0.010069 | -0.119549 | -0.092680 | 0.007332 | -0.108653 | -0.246026 | -0.051974 | -0.093387 | -0.005088 | 0.179644 | -0.122082 | 0.094056 | -0.111098 | 0.016984 | -0.028037 | 0.027459 | -0.128117 | -0.126827 | -0.004236 | 0.020453 | 0.117548 | -0.493684 | 0.380802 | -0.356318 | -0.345527 | -0.293632 | 0.021088 | -0.226347 | -0.131183 | -0.227184 | 0.533804 | 0.600246 | -0.011850 | -0.359462 | 0.067918 | 0.426015 | 0.529943 | -0.383941 | 0.102393 | 0.103441 | -0.109117 | -0.295097 | -0.182595 | 0.147071 | 0.016338 | -0.119312 | 0.058241 | -0.150590 | 0.063461 | 0.064448 | -0.166495 | 0.058106 | -0.077925 | -0.065756 | 0.021276 | 0.108737 | 0.030786 | -0.007117 | 0.022572 | -0.021833 | -0.036456 | -0.020514 | 0.008522 | 0.001721 | 0.007231 | -0.038603 | 0.043413 | -0.062598 | 0.049743 | 0.048378 | 0.043156 | 0.026021 | -0.013640 | 0.015648 | 0.018627 | -0.033522 | 0.023908 | 0.010241 | 0.000658 | 0.027045 | 0.006543 | 0.004898 | 0.000056 | -0.001713 | 0.019970 | -0.005259 | -0.017651 | 0.001101 | -0.000428 | -0.001169 | -0.001682 | 0.001803 | 8.253140e-13 | 3.036098e-12 | -1.282231e-14 | 4.822722e-13 | -5.724605e-13 | -8.322810e-14 | 3.717329e-14 | -4.263749e-14 | -1.219619e-12 | 6.586211e-13 | 1.358917e-12 | -4.520304e-13 | -1.495935e-13 | -3.053587e-13 | 6.970258e-13 | -6.801523e-13 | -7.709663e-13 | -6.007023e-14 | -8.788278e-14 | 3.634391e-13 | 3.427984e-13 | 1.478694e-14 | 4.376906e-13 | -2.124123e-13 | -1.441653e-13 | 2.924280e-13 | -3.475302e-13 | 2.044947e-13 | 5.594822e-13 | 0.000646 | 0.001351 | 0.003261 | -0.000449 | 0.000830 | 0.008345 | -0.000068 | 0.003175 | 0.016376 | -0.007787 | 0.003761 | -0.005638 | -0.011902 | 0.001815 | -0.034904 | 0.048006 | 0.075591 | 0.090641 | -0.006339 | -0.043793 | 0.021340 | -0.029072 | 0.044513 | 0.013373 | -0.083284 | -0.077810 | 0.052233 | 0.008271 | 0.021237 | -0.042025 | 0.198054 | 0.113263 | -0.058153 | 0.094471 | 0.228296 | -0.176797 | -0.122531 | 0.204857 | -0.378788 | -0.199944 | -0.363128 | -0.012757 | 0.139071 | -0.075139 | 0.253585 | -0.090229 | -0.217825 | 0.218308 | -0.267917 | 0.199734 | 0.094606 | 0.074341 | 0.103974 | -0.187454 | -0.243285 | 0.170159 | 0.468823 | 0.124462 | -0.274712 | 0.274267 | -0.545699 | 0.352986 | -0.046200 | 0.191029 | 0.390720 | 0.107193 | -0.183584 | 1.686666 | 4.013364 | 3.697415 | 2.309273 | 9.864420 | 1.593944 | 3.489672 | -0.785781 | 0.995907 | 1.091577 | 1.967451 | 1.967451 | 1.967451 | 1.757292 | 1.828258 | 1.828258 | 1.828258 | 1.757292 | 1.716682 | 1.716682 | 1.716682 | 1.684202 | 1.454577 | 1.454577 | 1.454577 | 1.684202 | 0.696360 | 0.519988 | 0.519988 | 0.519988 | 0.696360 | 0.527106 | 0.174842 | 0.174842 | 0.174842 | 0.527106 | 1.865922 | 2.481351 | 2.111799 | 1.518005 | 2.491871 | 1.725503 | 1.839066 | 1.895887 | 1.584613 | 2.135338 | 1.633639 | 1.793436 | 1.591288 | 3.791628 | 0.426820 | 2.293836 | 2.530565 | 1.559053 | 1.090102 | 2.382660 | 0.410595 | -0.855140 | 2.306451 | 2.021639 | 2.321210 | 3.178760 | -0.968738 | -0.770785 | -1.064489 | -0.543447 | -0.318908 | -0.074659 | -0.140241 | -0.318073 |
| 2 | -0.549461 | 0.549461 | -0.243320 | -0.200086 | 2.173241 | -0.157398 | -0.043311 | -0.159226 | -0.320339 | -0.221602 | -1.083970 | -0.007421 | -0.007421 | -0.007421 | -0.739256 | -0.007421 | 134.751623 | -1.183518 | -0.007421 | -0.007421 | -0.258813 | -0.017085 | -0.155351 | -0.099347 | -0.561520 | -0.133703 | 0.008739 | -0.211100 | -0.385805 | 0.249944 | -1.098549 | 0.653056 | -0.692954 | -0.125449 | -2.555751 | 3.164321 | -0.052203 | 1.098404 | -0.196219 | 0.460858 | -0.464040 | 2.615813 | 0.221279 | 1.488161 | 0.097318 | 1.035531 | -0.640831 | 3.362325 | -3.844559 | 1.125514 | -1.521700 | -0.723552 | -0.176947 | -0.775217 | -0.578675 | 1.033989 | -0.951941 | 0.369220 | 0.133379 | 0.098876 | 0.149616 | -1.490444 | 0.194185 | -0.329183 | -0.214546 | 0.224300 | -0.307572 | -0.026110 | -0.131600 | 0.223520 | 0.090770 | -0.076821 | -0.052729 | -0.052196 | -0.119112 | 0.116269 | 0.024458 | -0.026558 | -0.126343 | -0.197976 | 0.023151 | -0.260938 | -0.042340 | 0.072105 | -0.029533 | 0.036162 | -0.180051 | -0.064309 | 0.075104 | -0.106197 | -0.151694 | -0.231420 | -0.106367 | -0.031178 | -0.009255 | -0.065675 | -0.127407 | -0.191857 | 0.027734 | -0.283441 | -0.068381 | -0.126702 | -0.129831 | -0.111300 | -0.417849 | 0.155636 | 0.311076 | -0.300572 | 0.016992 | 0.274355 | -0.040008 | -0.238912 | -0.166051 | -0.254214 | -0.041283 | -0.098426 | -0.131329 | -0.326457 | -0.156098 | -0.011257 | -0.017541 | 0.021224 | 0.127563 | 0.101303 | -0.037059 | -0.154341 | -0.051848 | 0.262180 | -0.169659 | 0.210952 | 0.187486 | -0.827832 | -0.006134 | -0.083404 | 0.271356 | -0.557108 | -0.521545 | 0.000665 | 0.227176 | -0.078251 | 0.183786 | -0.100993 | 0.242239 | 0.222050 | 0.177046 | -0.533937 | -0.061600 | 0.304169 | -0.280282 | -0.089176 | -0.057149 | 0.034380 | -0.057564 | -0.091304 | 0.277372 | -1.440545 | -0.086590 | 0.372596 | -0.440936 | 0.043286 | -0.142858 | -0.788251 | -1.560627 | -1.358089 | 0.460762 | -0.257737 | 0.111929 | 0.540291 | -1.094658 | -0.444038 | -0.087696 | -1.772570 | 0.605983 | 0.818940 | -2.372275 | 0.689227 | 0.677397 | -1.374102 | -3.337659 | -5.023191 | 0.844339 | 0.982759 | 0.601445 | 1.100664 | 1.813789 | -1.714559 | -0.090288 | 1.538776 | 1.998732 | 2.086108 | -0.273292 | -1.747413 | 1.990532 | -0.363398 | 1.175752 | 3.586258 | 0.749065 | 3.728046 | 0.228683 | 0.934633 | 0.215514 | -2.508128 | -1.804746 | 4.427921 | 0.118978 | 0.055027 | 1.158042 | -1.499516 | 3.079435 | -0.775953 | -1.433359 | -0.246414 | -3.017052 | 1.986929 | 1.096578 | 0.747984 | 2.275328 | -5.606073 | 3.671009 | 1.489590 | -1.241723 | -3.749192 | -2.134990 | 8.933014 | 6.567214 | 0.032593 | -3.171463 | -0.538306 | -4.845864 | -1.255298 | -5.763620 | 4.106369 | -7.231835 | -3.165886 | -3.668339 | 0.931353 | -3.003468 | -4.123856 | -0.071417 | -2.660184 | -0.241473 | 2.695922 | -0.391085 | -0.309550 | -0.217746 | -0.244788 | 0.045692 | -0.207735 | -0.365621 | 0.315363 | -1.203033 | -0.468928 | -0.408268 | -0.420352 | -0.330091 | -0.207700 | 0.981062 | 0.319396 | 0.419477 | -0.162150 | -0.460295 | -0.235526 | 0.110202 | 0.727480 | 0.080552 | -0.661481 | -0.787856 | -0.150934 | -0.035326 | 0.083032 | 0.177802 | -0.142434 | 0.170785 | 0.038726 | 0.176184 | -0.057977 | 0.195241 | 0.240600 | 0.194533 | -0.047749 | 0.013702 | -0.099494 | 0.043197 | 0.008421 | 0.071515 | -0.261065 | 0.164758 | -0.204567 | 0.111747 | 0.224852 | 0.095345 | -0.165600 | -0.098104 | 0.185224 | -1.007427 | 0.383425 | -0.844146 | 0.595892 | 0.682144 | 0.536070 | -0.048185 | 0.385394 | 0.886149 | -0.180272 | 0.679478 | -0.272291 | -0.061443 | 0.372345 | -0.357528 | 0.065238 | -0.265427 | -0.110998 | -0.033170 | 0.184583 | -0.193668 | 0.258645 | -0.214488 | -0.135857 | 0.304798 | -0.146043 | -0.552069 | 0.131506 | 0.037543 | 0.080747 | 0.025523 | 0.064832 | -0.037481 | 0.065358 | -0.192135 | -0.066773 | 0.075565 | -0.007293 | 0.009434 | -0.045009 | 0.019042 | 0.000184 | -0.124571 | 0.022683 | -0.001122 | 0.007883 | 0.041079 | 0.061792 | 0.043677 | 0.007882 | -0.082375 | 0.037232 | 0.003298 | 0.007174 | 0.061770 | 0.077339 | -0.079842 | -0.007876 | -0.000433 | 0.000739 | -0.005631 | 0.004462 | -0.009006 | -0.000520 | 0.000470 | -0.001306 | -0.000481 | -0.000103 | 6.595787e-14 | 5.572357e-13 | 1.493610e-13 | 1.861709e-13 | -1.458207e-13 | 1.938398e-13 | 4.905571e-13 | -3.348726e-13 | -4.624119e-13 | 4.307896e-13 | 7.588101e-13 | -1.736184e-13 | 5.680962e-13 | -2.892674e-13 | 2.425362e-13 | 2.819889e-13 | -6.391905e-13 | -2.246249e-13 | -2.430504e-13 | -1.775977e-13 | 7.003689e-14 | 3.942614e-13 | -1.315704e-13 | -4.112645e-13 | 4.396000e-13 | 4.707710e-13 | -1.988568e-13 | 7.566560e-14 | -1.218159e-15 | 0.000304 | 0.000227 | 0.001718 | 0.003683 | -0.006668 | 0.013104 | 0.004652 | 0.007286 | 0.011800 | -0.000609 | -0.000471 | 0.009785 | -0.008856 | -0.051621 | 0.013161 | 0.068941 | 0.047872 | 0.036074 | -0.053992 | -0.160231 | 0.041248 | -0.108446 | -0.006411 | 0.048080 | -0.053402 | -0.053870 | -0.204285 | 0.153997 | 0.013815 | -0.113353 | -0.039425 | -0.145924 | -0.333918 | 0.043463 | -0.278368 | -0.310839 | -0.138256 | -0.042843 | 0.353198 | -0.252925 | -0.081981 | -0.385320 | -0.021495 | 0.280847 | 0.318372 | -0.507378 | 0.214363 | -0.423207 | -0.486062 | -0.073222 | -0.583009 | -0.064513 | -0.660985 | 0.708667 | -0.263698 | 0.221439 | 0.245657 | -0.499414 | -0.009785 | 0.005127 | 0.424804 | -0.511147 | -0.067987 | -0.069496 | -0.111682 | -0.021870 | 0.185835 | 0.187878 | 3.724114 | 3.534396 | 1.997752 | 9.864420 | 3.108090 | 3.489672 | -0.597693 | -0.891177 | -1.024769 | 1.620283 | 1.620283 | 1.620283 | 1.757292 | 1.779978 | 1.779978 | 1.779978 | 1.757292 | 1.764859 | 1.764859 | 1.764859 | 1.684202 | 1.454577 | 1.454577 | 1.454577 | 1.684202 | 0.696360 | 0.470696 | 0.470696 | 0.470696 | 0.696360 | 0.477217 | -0.114632 | -0.114632 | -0.114632 | 0.477217 | 1.593650 | 2.122842 | 0.558238 | 1.722099 | 2.322322 | 2.148563 | 2.219609 | 2.525239 | 1.649855 | 2.195268 | 1.968594 | 1.725175 | 2.200811 | 3.569658 | 1.417214 | 1.423639 | -0.345984 | 1.118643 | -1.299069 | 1.811528 | 0.007543 | -0.516916 | 1.999208 | 2.375083 | 2.066492 | 3.091316 | -1.018980 | -1.093911 | -0.594753 | -0.430420 | -0.437206 | -0.074659 | -0.081536 | -0.318073 |
| 3 | -0.549461 | 0.549461 | -0.243320 | -0.200086 | -0.460142 | -0.157398 | -0.043311 | -0.159226 | -0.320339 | -0.221602 | 0.922535 | -0.007421 | -0.007421 | -0.007421 | 1.352711 | -0.007421 | -0.007421 | -1.183518 | -0.007421 | -0.007421 | -0.258813 | -1.613048 | 2.113955 | 0.781513 | -0.041090 | 0.284282 | -0.038338 | -0.676021 | -1.427084 | -0.080922 | 3.231922 | 1.295412 | 0.012508 | 0.332316 | 0.626892 | 0.607942 | 0.036492 | 0.229525 | 0.042520 | 0.390681 | 0.062942 | -0.044912 | 0.096292 | 0.095894 | -0.017115 | 0.124980 | -0.027133 | 0.025587 | -0.036045 | 0.104323 | 0.062240 | -0.069196 | 0.049094 | -0.004196 | 0.075716 | 0.044748 | -0.003177 | -0.075337 | -0.000055 | 0.105245 | -0.048945 | -0.061112 | -0.038348 | -0.031764 | -0.054545 | -0.057354 | -0.121143 | 0.013155 | -0.007405 | -0.030269 | 0.047155 | -0.016913 | 0.092906 | -0.039380 | -0.077470 | -0.082177 | -0.037030 | 0.173840 | -0.171706 | -0.016847 | 0.016145 | -0.062745 | -0.022843 | -0.015480 | 0.027843 | -0.041203 | 0.003861 | -0.008904 | -0.046435 | -0.061257 | -0.046170 | -0.038983 | -0.051279 | -0.072083 | 0.056905 | -0.002039 | 0.001940 | 0.039230 | 0.003453 | 0.304404 | 1.413800 | -0.526698 | -0.375402 | 0.109597 | -0.423688 | -0.035690 | -0.089234 | -0.456109 | 0.205518 | -0.009879 | -0.199800 | -0.843242 | -1.662875 | -1.626516 | 0.353621 | -3.060685 | -1.887535 | -3.938735 | -0.084146 | 0.777366 | -1.160367 | -2.086786 | -3.793884 | 2.372960 | 17.346957 | 0.323497 | 7.185653 | 1.716609 | 0.493613 | -7.657147 | 4.487332 | 5.395448 | -2.494822 | -2.274507 | 0.867822 | 0.654820 | 0.814867 | -0.011625 | -0.329482 | 0.196535 | 0.304050 | -0.063660 | 1.023886 | 0.354228 | -0.874284 | 0.076024 | -0.260614 | -0.520658 | -0.654328 | -0.061348 | 0.259709 | 0.510558 | 0.742844 | -0.396823 | -0.282553 | 0.314588 | 0.097260 | 0.054746 | 0.129444 | 0.039219 | 0.011476 | 0.229740 | 0.347145 | 0.139064 | -0.038718 | 0.089252 | -0.076511 | 0.001235 | 0.034633 | 0.043739 | -0.017059 | -0.046728 | -0.018559 | 0.000869 | -0.025033 | -0.050967 | -0.008663 | 0.032284 | 0.000171 | 0.031409 | 0.016561 | 0.008224 | 0.010151 | -0.008566 | 0.015066 | -0.008863 | 0.066714 | -0.040589 | -0.009727 | -0.005400 | -0.036307 | -0.000109 | -0.020808 | -0.018713 | -0.001896 | 0.014031 | 0.010852 | -0.018255 | 0.048484 | 0.018804 | 0.008596 | 0.004784 | 0.020417 | 0.015194 | -0.033876 | -0.016839 | -0.025777 | -0.005566 | -0.041549 | -0.031028 | -0.016025 | -0.038980 | -0.030797 | 0.020078 | 0.012062 | 0.040959 | -0.008658 | 0.011459 | 0.007490 | 0.026479 | -0.189486 | -0.210200 | 0.011898 | -0.014409 | -0.101808 | 0.075313 | 0.206237 | -0.077441 | 0.201667 | 0.069879 | 0.079310 | 0.142394 | -0.192540 | -0.021895 | -0.111482 | -0.326456 | -0.042605 | -0.208090 | 0.107562 | 0.066910 | -0.273328 | 0.050957 | 0.069328 | 0.074669 | 0.395907 | 0.004283 | 0.162800 | -0.221290 | -0.041560 | 0.189613 | 0.257747 | -0.024757 | 0.250510 | -0.319735 | 0.076783 | -0.275395 | -0.132746 | 0.117053 | 0.066243 | -0.116763 | -0.027526 | -0.214193 | 0.040811 | 0.055356 | -0.026428 | 0.032285 | -0.048165 | -0.038001 | -0.126100 | 0.107319 | -0.030829 | 0.126672 | 0.056959 | -0.029922 | 0.036021 | -0.045177 | -0.014616 | -0.149680 | 0.040414 | -0.034874 | 0.059327 | -0.114990 | 0.084580 | 0.004520 | -0.010172 | 0.184346 | 0.256402 | -0.080829 | 0.032665 | 0.135759 | 0.021214 | 0.000104 | 0.102261 | 0.143784 | 0.051508 | 0.020533 | -0.081731 | 0.155202 | 0.086739 | 0.155264 | -0.093700 | 0.038151 | 0.104730 | 0.023600 | 0.023212 | 0.000399 | 0.017778 | 0.085027 | -0.009863 | 0.095550 | -0.040690 | -0.044875 | 0.051347 | 0.019507 | -0.064203 | 0.041495 | -0.046404 | -0.048103 | 0.013467 | -0.002091 | -0.051455 | 0.028990 | 0.022104 | -0.008755 | 0.020917 | 0.001462 | 0.024211 | -0.014315 | -0.020055 | -0.005578 | 0.023377 | -0.003179 | 0.013755 | -0.003950 | 0.009567 | -0.010010 | -0.003680 | 0.003423 | 0.004497 | -0.001572 | -0.001222 | -0.004080 | 0.010599 | -0.009886 | -0.001997 | -0.008885 | 0.016906 | 0.004692 | 0.004702 | -0.000350 | -0.013489 | 0.004569 | 0.001811 | 0.001931 | -0.000296 | 0.007719 | 0.000198 | -0.000149 | 0.000294 | 0.000246 | -0.001287 | -0.000022 | 5.403858e-13 | 1.871688e-12 | -1.848559e-14 | 7.747310e-13 | 1.130374e-13 | -3.566032e-13 | -2.856897e-14 | 3.142888e-13 | -2.752260e-13 | 3.778185e-13 | 5.790726e-13 | 2.552148e-13 | 3.743538e-14 | 1.696583e-13 | -2.320552e-13 | 1.695277e-13 | -5.041000e-13 | 6.517461e-13 | 6.850483e-14 | 3.872802e-13 | 1.045042e-13 | 4.540865e-13 | 3.039817e-13 | -5.029946e-13 | 2.280004e-13 | 1.057661e-12 | -1.686482e-14 | 4.138114e-13 | 4.350187e-13 | 0.000898 | 0.000064 | -0.000592 | 0.000722 | 0.002130 | -0.002865 | -0.007820 | 0.002377 | 0.002385 | -0.004110 | -0.000110 | -0.015326 | 0.006797 | -0.066191 | -0.010088 | -0.001763 | 0.003451 | 0.035864 | -0.024105 | -0.026619 | -0.001218 | -0.039864 | 0.041715 | 0.013126 | -0.074898 | -0.036987 | 0.048937 | -0.004382 | -0.156708 | 0.005329 | 0.001246 | 0.125010 | 0.064376 | -0.006293 | -0.047094 | -0.097946 | 0.021965 | -0.097069 | 0.010510 | -0.028651 | 0.024781 | 0.071788 | 0.036134 | -0.135334 | -0.066450 | -0.091750 | 0.070086 | -0.055203 | 0.005229 | 0.006194 | -0.154561 | -0.169844 | 0.041748 | -0.063338 | -0.202698 | 0.071541 | 0.020087 | -0.091235 | -0.146323 | -0.156195 | 0.107056 | -0.112310 | 0.218827 | 0.102852 | 0.125162 | -0.151908 | 0.075588 | 0.401990 | 3.579489 | 3.534396 | -0.465097 | 7.326477 | 0.079797 | -1.800331 | -1.161957 | 1.750741 | 0.129602 | -2.545731 | -2.545731 | -2.545731 | -2.517663 | -2.516977 | -2.516977 | -2.516977 | -2.517663 | -2.522831 | -2.522831 | -2.522831 | -2.554472 | -2.565223 | -2.565223 | -2.565223 | -2.554472 | -2.551047 | -2.486788 | -2.486788 | -2.486788 | -2.551047 | -2.516127 | -2.382176 | -2.382176 | -2.382176 | -2.516127 | -1.782517 | -1.667116 | -1.800873 | -0.590969 | -1.690356 | -1.976272 | -1.422733 | -1.365303 | -0.111691 | -0.981022 | -0.510075 | -0.459193 | -0.237281 | 3.125717 | -1.483228 | -1.419003 | 0.161642 | -1.272151 | -0.104484 | -1.822948 | -1.028876 | -0.806823 | -1.943748 | 1.031994 | -0.544371 | 0.817758 | -1.621878 | -1.232393 | -1.534226 | 4.147180 | 4.058124 | 4.288340 | 4.203960 | 4.304691 |
| 4 | -0.549461 | 0.549461 | 4.109822 | -0.200086 | -0.460142 | -0.157398 | -0.043311 | -0.159226 | -0.320339 | -0.221602 | -1.083970 | -0.007421 | -0.007421 | -0.007421 | -0.739256 | -0.007421 | -0.007421 | 0.844939 | -0.007421 | -0.007421 | -0.258813 | -0.091062 | -0.142138 | -0.155704 | -0.290690 | 0.018882 | 0.023538 | 0.088675 | -0.259151 | 0.118454 | -0.277223 | 0.113376 | -0.030246 | -0.012391 | -0.296987 | -0.305950 | 0.638791 | -0.402836 | 0.095289 | -0.677695 | -0.323365 | 2.921820 | -0.528324 | -0.916167 | 0.784182 | -2.881635 | -4.046828 | -1.403529 | 0.811813 | -0.457896 | -1.484107 | 0.336951 | 0.651645 | -0.199662 | -0.009815 | -0.996969 | 1.291357 | 0.552010 | -0.001381 | 0.759154 | 6.402136 | 0.014643 | -4.568361 | -0.669343 | -0.688380 | -0.916656 | -0.460104 | -0.289265 | -1.162298 | 0.393361 | 0.214624 | 0.007462 | 0.046069 | -0.162350 | -0.066949 | 0.125466 | 0.056490 | -0.144479 | -0.154396 | -0.238182 | 0.140208 | -0.092793 | 0.090896 | 0.107455 | -0.228375 | -0.123853 | -0.045649 | -0.096664 | 0.029720 | -0.013703 | 0.025540 | 0.009465 | 0.436134 | -0.207360 | 0.023093 | -0.133724 | -0.282796 | -0.306715 | 0.035326 | -0.189899 | -0.043495 | -0.135987 | 0.025136 | -0.120619 | -0.229064 | 0.199400 | 0.396874 | 0.200408 | -0.227465 | 0.042851 | -0.035421 | 10.683493 | 2.498634 | 1.421645 | -1.076819 | -0.811425 | 0.223074 | 5.204287 | -3.543576 | 2.357343 | 11.674996 | 0.966778 | 4.815652 | -5.562447 | -2.146573 | 6.493908 | 5.744070 | 2.263234 | -4.018663 | -3.833655 | 3.351597 | 1.992660 | -4.387845 | -1.353734 | -1.154061 | 0.345595 | 0.172170 | -1.123522 | -2.063017 | 0.341783 | -0.902381 | 1.248841 | -1.009345 | 0.932496 | 0.776984 | -0.960601 | 0.800480 | -0.117982 | 0.075546 | 0.405736 | 0.643886 | 0.206377 | 0.284722 | 0.039798 | -0.485481 | 0.322969 | 0.596088 | -0.173772 | 0.133415 | 1.125293 | -0.347591 | 0.702950 | 1.358908 | -1.904851 | 0.079878 | 0.238077 | 0.186989 | 0.109801 | -0.030554 | -0.035435 | -0.126706 | 0.032382 | -0.081465 | -0.148267 | 0.075632 | -0.023674 | -0.059222 | -0.016287 | 0.092370 | 0.035201 | -0.026172 | 0.028214 | 0.001353 | 0.041429 | 0.003150 | 0.006559 | 0.030333 | -0.001168 | -0.043496 | -0.025036 | -0.011054 | 0.002166 | -0.053098 | 0.008663 | -0.005129 | 0.035345 | -0.014254 | 0.009067 | 0.044906 | -0.047469 | -0.021653 | 0.000397 | 0.043235 | -0.007156 | 0.037646 | 0.010363 | -0.014186 | -0.039908 | -0.047054 | -0.052000 | -0.045913 | 0.079196 | -0.028701 | 0.007140 | 0.000791 | 0.023369 | 0.026717 | 0.047993 | 0.052964 | 0.110131 | -0.125628 | -0.037942 | -0.108221 | 0.018023 | 0.124822 | 0.097621 | 0.147474 | 0.097956 | -0.015887 | -0.138383 | -0.070688 | 0.060297 | -0.094765 | -0.372871 | -0.104256 | 0.055191 | -0.089231 | 0.032969 | 0.175892 | 0.080074 | -0.149253 | 0.041967 | -0.336697 | -0.024249 | 0.127932 | 0.011851 | 0.072007 | -0.212062 | -0.208177 | -0.339806 | 0.279147 | 0.051760 | 0.237822 | 0.077641 | -0.006038 | 0.215633 | -0.205767 | -0.107031 | -0.076876 | -0.051448 | -0.120190 | -0.071160 | -0.042202 | -0.022066 | 0.015799 | 0.113661 | -0.034110 | 0.122412 | -0.004128 | -0.104020 | 0.001683 | -0.034192 | -0.031496 | 0.007867 | -0.047550 | -0.056216 | 0.097000 | -0.136182 | 0.017754 | 0.108677 | 0.106493 | 0.039514 | -0.066111 | 0.057269 | 0.182240 | 0.054177 | 0.086463 | 0.053867 | -0.017969 | -0.020106 | 0.024826 | 0.116873 | -0.211760 | -0.111568 | 0.044271 | -0.124540 | 0.123316 | 0.064723 | 0.190813 | -0.132186 | -0.133371 | -0.377998 | 0.065018 | 0.027507 | -0.624117 | -0.023784 | 0.528061 | 0.033043 | 0.190279 | -0.029297 | 0.039950 | -0.169085 | 0.037868 | 0.319105 | -0.066247 | -0.140469 | -0.144764 | 0.279565 | -0.095480 | -0.180349 | -0.136198 | 0.067375 | 0.200282 | 0.035690 | 0.074034 | 0.032853 | -0.002653 | 0.128821 | -0.104578 | -0.012650 | 0.026667 | 0.030600 | 0.026710 | -0.012186 | 0.000604 | -0.044312 | 0.047991 | -0.008702 | -0.000343 | -0.037231 | -0.045570 | 0.002996 | 0.022491 | -0.013916 | -0.018276 | -0.004874 | -0.000790 | 0.006506 | -0.006051 | -0.007285 | 0.006555 | 0.004848 | -0.002896 | -0.010982 | 0.020604 | -0.006821 | -0.006159 | -0.001374 | -0.000871 | -0.000553 | -0.001962 | 0.001128 | 8.134414e-13 | 3.343869e-12 | 3.403775e-13 | 1.247558e-12 | -5.078834e-13 | -7.881740e-13 | 2.150963e-13 | 3.019587e-13 | -1.342819e-12 | 8.734203e-13 | 1.534630e-12 | -1.445148e-13 | 1.707971e-14 | -7.835449e-14 | 2.055861e-13 | -5.659225e-13 | -8.610126e-13 | 1.877626e-13 | -1.848306e-13 | 5.592423e-13 | 4.748970e-14 | 4.573081e-13 | 4.096153e-13 | -3.742568e-13 | -1.916247e-13 | 9.886954e-13 | -8.823566e-14 | 4.101260e-13 | 8.240556e-13 | 0.001114 | 0.001579 | 0.003688 | -0.000778 | 0.000855 | 0.009859 | -0.002172 | 0.006321 | 0.018739 | -0.008307 | -0.001476 | -0.017744 | 0.068083 | -0.003997 | -0.035762 | 0.049756 | 0.022789 | 0.009348 | -0.048196 | -0.012751 | 0.049627 | 0.074143 | 0.044665 | 0.040591 | -0.001128 | -0.020650 | 0.091526 | -0.162773 | -0.043332 | 0.041396 | -0.008670 | 0.013198 | 0.135069 | 0.188542 | -0.064135 | 0.108226 | 0.198994 | -0.105229 | -0.055194 | 0.036052 | -0.393483 | 0.024604 | -0.077289 | -0.061464 | -0.079109 | -0.056735 | -0.124512 | 0.146562 | 0.023865 | 0.033096 | -0.099578 | -0.150772 | 0.081821 | 0.116195 | 0.095288 | 0.114041 | 0.259925 | -0.158098 | -0.201357 | 0.106939 | 0.300085 | 0.046780 | -0.287300 | -0.043393 | 0.042920 | -0.013065 | 0.006991 | 0.401990 | 3.579489 | 3.371377 | 2.503515 | 7.326477 | 3.108090 | 2.167171 | -0.785781 | -0.136343 | -0.768242 | 1.521092 | 1.521092 | 1.521092 | 1.661226 | 1.683417 | 1.683417 | 1.683417 | 1.661226 | 1.716682 | 1.716682 | 1.716682 | 1.684202 | 1.752340 | 1.752340 | 1.752340 | 1.684202 | 1.295881 | 1.308650 | 1.308650 | 1.308650 | 1.295881 | 1.125774 | 0.802035 | 0.802035 | 0.802035 | 1.125774 | 2.356010 | 1.866764 | 0.155463 | 2.266350 | 2.209288 | 1.619738 | 2.056519 | 2.296384 | 2.498007 | 1.955548 | 0.896737 | 0.769514 | 1.049490 | 3.236703 | 0.922017 | 2.061783 | -0.176775 | 1.684884 | 0.771546 | 2.278818 | 1.159120 | 0.691027 | 1.896793 | 2.869906 | 1.939132 | 2.566649 | 1.040923 | 0.475556 | 0.250772 | -0.091339 | -0.200610 | -0.680632 | -0.375063 | -0.206681 |
def get_boxpolt_info(data, size=[10,5], axis_rotation=0):
plt.rcParams['figure.figsize'] = size
ax = sns.boxplot(data=data)
plt.title('Numerical Features Distribution')
if axis_rotation:
plt.xticks(rotation=axis_rotation)
plt.show()
get_boxpolt_info(df_ordinal[num_col_names], size=(20, 5), axis_rotation=90)
get_boxpolt_info(df_ordinal[cat_col_names], size=(20, 5), axis_rotation=90)
df_ordinal.reset_index(inplace=True, drop=True)
df_dummies.reset_index(inplace=True, drop=True)
label.reset_index(inplace=True, drop=True)
df_ordinal_final = df_ordinal.join(label)
df_ordinal_final.to_csv('./data/processed/1_1_processed_ordinal_encoding.csv', index_label=False)
df_dummies_final = df_dummies.join(label)
df_dummies_final.to_csv('../data/processed/1_1_processed_onehot_encoding.csv', index_label=False)